Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

AstraZeneca-Sanger Drug Combination DREAM Consortium

Research output: Contribution to journalArticleAcademicpeer-review

116 Citations (Web of Science)

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Original languageEnglish
Article number2674
Number of pages17
JournalNature Communications
Volume10
DOIs
Publication statusPublished - 17 Jun 2019

Keywords

  • ANDROGEN RECEPTOR
  • BREAST-CANCER
  • GENE
  • CELL
  • INHIBITION
  • RESISTANCE
  • PATHWAY
  • MUTATIONS
  • LANDSCAPE
  • RESOURCE

Cite this